code: def setUp(self):
      super(ModelBasedAblationFlipTest, self).setUp()
      self.ablation_flip = ablation_flip.AblationFlip()
  
      # Classification model that clasifies a given input sentence.
      self.classification_model = glue_models.SST2Model(BERT_TINY_PATH)
      self.classification_config = {
          ablation_flip.PREDICTION_KEY: "probas"
      }
  
      # Clasification model with the 'sentence' field marked as
      # non-required.
      self.classification_model_non_required_field = (
          SST2ModelNonRequiredField(BERT_TINY_PATH)
      )
  
      # Clasification model with a counter to count number of predict calls.
      # TODO(ataly): Consider setting up a Mock object to count number of
      # predict calls.
      self.classification_model_with_predict_counter = (
          SST2ModelWithPredictCounter(BERT_TINY_PATH)
      )
  
      # Regression model determining similarity between two input sentences.
      self.regression_model = glue_models.STSBModel(STSB_PATH)
      self.regression_config = {ablation_flip.PREDICTION_KEY: "score"}
  
imports: from collections.abc import Iterable, Iterator
  from absl.testing import absltest
  from lit_nlp.api import types
  from lit_nlp.components import ablation_flip
  from lit_nlp.examples.models import glue_models
  import numpy
  from lit_nlp.lib import file_cache
label_desc: Use image node_id 1 for calling the ModelBasedAblationFlipTest obj's underlying member method code with example usage: obj.setUp() without return types